Attempts have been made to model virtual biological tissues exhibiting adequate fidelity with living tissue. [Stylapaulas, N., Cotin, S., Dawson, S., Ottensmeyer, M., Neumann, P., Bardsley, R., Russell, M., Jackson, P., Rattner, D. (2002) CELTS: A Clinically-Based Computer Enhanced Laparoscopic Training System, The Simulation Group Massachusetts General Hospital-CIMIT, 65 Landsdowne Street, Cambridge, Mass. 02139: 2-3, 6-7; Crossman, A., Brewster, S., Reid, S., Mellor, D. (2000) Multi-Session VR Medical Training—The HOPS Simulator, Department of Computing Science, Faculty of Veterinary Medicine, University of Glasgow G12 8QQ, Glasgow, UK: 3; Chabanas, M., Payan, Y., Marecaux, C., Swider, P., Boutault, F. (2003) Comparison of Linear and Non-Linear Soft Tissue Models with Post-Operative CT Scan in Maxillofacial Surgery, Laboratoire TIMC-IMAG, CNRS UMR 5525, Université Joseph Fourier—Grenoble Institut d'Ingénierie de l'Information de Santé (In3S), 38706 La Tronche cedex, France Service de chirurgie maxillo-faciale et plastique de la face, Hôpital Purpan Toulouse Place Baylac BP 3103, 31059 Toulouse Cedex 3, France Laboratoire de Biomécanique, EA 3697, Université P. Sabatier, Hôpital Purpan Amphithéâtre Laporte, Place Baylac BP 3103-31059 Toulouse cedex 3, France: 1; Sengers, B. G. (2005) Modeling the development of tissue engineered cartilage, Technische Universiteit Eindhoven, Eindhoven, Nev.: 5; ElHelw, M. A., Lo, B. P., Darzi, A., Yang, G. (2004) Real-Time Photo-Realistic Rendering for Surgical Simulations with Graphics Hardware, Royal Society/Wolfson Medical Image Computing Laboratory, Imperial College London, London, United Kingdom: 2] Lack of adequate fidelity hinders medical and biological research, simulation, and practice. Lack of fidelity is realized in two distinct ways:                poor or limited simulation of tissue response to or recovery from external stimuli (e.g., surgery, drug treatment), and        poor or limited simulation of tissue development and internal processes (e.g., metabolism, homeostasis, aging, disease).Further, current models and their computational engines lack a comprehensive and biologically realistic basis and instead rely on incomplete, static, or otherwise inadequate assumptions that do not encompass the range or complexity of biological relationships necessary to properly generate the models. Specifically, lack of such support in current genetic algorithm (GA), evolutionary computing (EC), and biologically inspired computing (BIC) techniques and practices provides an opportunity for improving both the art of biological tissue modeling and the state of computer science and technology.Tissue Modeling Fidelity:        
Most models of biological tissues are based on principles of systems engineering [Sun and Lal, 2004]. For example, tissue structure and elasticity can be modeled as dampened springs, electrically excitable tissues as core conductors, and tissues such as blood according to principles of fluid mechanics. As different as these models are, they share a number of general features: they are constructed from the perspective of an external observer and designer of the system; they are grounded in laws (Hook, Kirchoff, Ohm, Bernoulli, etc.) that describe predictable behavior of the physical world in a manner that can be verified empirically, by measurement; they incorporate feedback controls to optimize system performance by tuning of adjustable elements; their complexity requires some kind of computational approach.
Although models based on a systems engineering approach contain a few features that mimic the way that natural living systems are built and how they function, such models fail to capture important emergent properties of natural systems.
Current tissue models are based on a deterministic, top-down approach, whereby all features must be incorporated into a pre-specified plan conceived and constructed by an intelligent designer. Top-down tissue models are based on analysis of static or time-averaged images using zoom focus, viewing an object with increasingly fine resolution where the locations of certain attributes or properties are plotted at specific locations on a map of the object of interest. They emphasize the anatomy of the organ as it exists, but they fail to capture where the organ came from and how it developed. Top-down modeling is appropriate for designing buildings, machines, or other objects of human engineering wherein form and function are imposed by external agents. However, living organisms are not machines and so top-down approaches fail to capture and leverage essential emergent functions of self-organizing systems such as self-construction via development (ontogeny), self-repair, metabolism, homeostasis, and adaptability (the ability to monitor and respond to complex, unpredictable environments).
TABLE AGeneral features of natural systems compared to human-designed systemsNatural SystemsHuman-engineered SystemsDesignSelection by evolutionaryOptimization by architectprocessConstructionSelf-constructs byBuilt by a separate processdevelopment; continuousand apparatus prior toturnover of componentsoperationControlFeedback, homeostasis,Automated feedbackself-repair, regenerationTuning/Contingent, adaptable andTask-specific; monitors onlyOperationplastic; monitors complex,a few parametersunpredictable environmentEvolutionary Computing and Biologically Inspired Computing:
Existing computational models for virtual representation of biological designs rely on relatively simplistic, not fully accurate, or narrow perspectives about living organisms and the processes by which they are constructed. Existing models appear to incorporate genes, genotype, phenotype, emergence, self-repair, and other biologically derived features, but upon closer inspection definitions of these terms have been broadened or changed so that their meaning and relevance to living systems is lowered [Forbes, 2004; Otter and Davis, 2004]. For example, the “crossover” mechanism used in evolutionary computation to effect reshuffling of genes between individuals bears little resemblance to any biological process: crossover conflates three different biological processes, namely, random segregation of chromosomes during meiosis, random fertilization, and a phenomenon that biologists call “crossing over” which takes place within a single individual [Hartwell et al., 2004]. Thus, the term “crossover” has been assimilated without the underlying complexity and richness of biological mechanisms [Back et al., 1997].
Deficiencies in terminology are particularly noticeable regarding the cellular basis of development, which is the primary domain of biology that supports the present invention. A few examples are outlined below.
Genotype→Phenotype Mapping
A fundamental shortcoming of conventional GA approaches is simplistic modeling of the relationship between phenotype and genotype. Problems with genotype-phenotype mapping can be traced to two main sources:                1. Inconsistent and broad usage of the term ‘gene’, and its derivatives (genetic);        2. Inadequate models for extrapolating from a single gene and what it encodes to a complete set of genes and what they specify collectively.        
In evolutionary computation the term “gene” refers to a broad range of objects, including: simple bit strings where each position is equivalent; coded representations of phenotypic characters; representations of characters that are subject to control, as individual genes or in groups; genes that encode either phenotypic characters or agents that control the activity of other genes. These are not equivalent meanings, yet they are grouped under the heading of genetic encodings.
The above definitions are based on simple, direct mapping of genotype onto phenotype, a corollary to the dogma that genotype defines the phenotype. The central dogma outlines the steps involved in expression of a single gene, but this simple 1:1 mapping does not explain the global relationship between genotype and phenotype. As recent cloning experiments confirm [Shin et al., 2002], genotype is not adequate to specify phenotype. Genotype does not specify when or where genes are transcribed, how proteins assemble into macromolecules, signaling or metabolic networks, nor any other aspect of cellular function on a higher level of organization. In modeling, a simple gene-based perspective is not adequate because it fails to capture the interaction of gene products, control of gene expression, cell signaling, epigenetic processes, and other complexities of biological systems.
Developmental Paradigms
Biological development is the process that best captures the power of biology. During human development a single cell, the fertilized egg, is transformed over a period of 38 weeks into a complex, integrated creature containing ˜10 trillion (1013) cells, as the simple instructions encoded in the egg and sperm produce a fully formed, if miniature, human being [Boal, 2002]. This description highlights two aspects, the potential of scale, and the potential of building complexity from simplicity. As seductive as this view may be, taken literally it is misleading and untrue, because it suggests that all of the complexity of a human being is bound up in its genetic code.
During development the embryo is constructing itself, and part of this process involves shaping an environment suitable for development and monitoring it. Genes determine the types of sensors a cell can make, but once a cell builds and deploys sensors, it begins to collect information that is not genetically encoded. Living organisms produce, detect, and record such signals and recognize patterns that are crucial to survival and reproduction. The complex relationships among genetically encoded components, development, and phenotypic plasticity of living organisms are not captured in current models, primarily because the models have been designed from a gene-centered perspective.
A living body, once constructed, remains in a dynamic, continual state of renewal and repair, in spite of its deceptively static appearance [Harris, 1987]. As old parts wear out or become damaged, they must be replaced. This implies that there is a turnover of materials and even more, mechanisms for checking the condition of parts (damage or error detection/proofreading), mechanisms to remove the damaged ones, and mechanisms to replace them. Current tissue models do not include these functions.
While in a sense biological development is the process of constructing an organism, construction usually is directed by an intelligent agent, whereby shape is imposed. Using this convention, top-down models appear more like buildings or perhaps blueprints of buildings than they do living organisms. Yet living systems are capable of self-construction using available resources, and the continuous recycling and replacement of old, worn, or damaged components produces “anatomical homeostasis” [Harris, 1987]. Accordingly, a living organism is more a fountain than a statue: both have definite form, but the fountain's form is incidental to the flow of materials though it, and in the statue, form is primary and static. Current models rely on form to map function. A failure to recognize the inadequacy of the statue metaphor impedes the ability to construct appropriate models of living organisms. A living body remains in a dynamic state, and so too, should virtual tissues generated by modeling.
Living organisms are remarkably resilient, fault tolerant, and during development, convergent on a desirable outcome, healthy offspring. In contrast, systems such as L-systems (grammar based), Turing Machines, and Cellular Automata (CAs) are typically not fault tolerant or robust in function. These systems are brittle in the sense that a slight perturbation in either initial conditions or in the rules of the system often leads to drastic changes in final outcome. Current computational systems lack fault tolerance because they are based on prescribed functions rather than processes that derive from interaction of versatile components.
The present invention represents an improvement over prior disclosures primarily from the perspective of higher ordered emergent functionality having been achieved. For example, it is submitted that the term “emerge” and its derivatives as used in U.S. Pat. No. 6,360,191 refers principally to the fact that a suitable solution to a design problem was found (“emerged” as stated in the patent) through an evolutionary search process. By contrast, the present invention has achieved truly adaptive emergent functionality in the forms of cell signaling, feedback, repair and even oscillatory behavior in accordance with biological fidelity.
Additionally, the disclosures of U.S. Pat. No. 6,360,191 and U.S. Pat. No. 5,867,397, referenced therein, imply that there is a limited and direct correspondence between genotype and phenotype, whereas the instant invention improves the representation of environmental factors and the delicate balance that exists as feedback between genome, environment and phenotype as manifest in gene expression, metabolism, cell signaling, sensory processes and gene regulation that actually form the basis for the present invention's premise, and upon which substantial genetic and biological evidence exists.
While the relatively straight-forward process of chromosomal-like splicing and resulting sexual “crossover” of U.S. Pat. No. 6,360,191 fit the genetic programming (referred to therein as a hierarchical genetic algorithm) process, the instant invention is submitted to improve the level of reference to the broader effect of environmental factors providing a more complete and descriptive genetic operation, with a broader approach as compared to the prior disclosures' narrower field of complex structural design achieved through a simulated natural selection process.
As in the above discussion of U.S. Pat. No. 6,360,191, the present invention is contrasted with U.S. Pat. No. 6,148,274 primarily from the perspective that the instant invention is seen to provide improvement in that adaptive emergent functionality was achieved. Also, as compared to the disclosure thereof, the instant invention provides a more logically faithful process by which the present invention carries out its evolutionary search function. In contrast, the disclosure of the referenced patent obtains solution vectors such as those deduced from user preferences and through the use of optimization adjustment techniques, the methods therein disclosed aspire to enhance local solution search abilities within the conventional genetic algorithm framework.
Furthermore, the referenced disclosure of U.S. Pat. No. 6,148,274 deals principally with the view that biological evolution occurs solely within a sexual “crossover” context, which is not accepted as a supported premise in the model of the instant invention.
The present invention is distinct from U.S. Pat. No. 6,701,231 based on its stated field of application, namely the optimization of control systems as well as its fundamental approach toward solving that class of problems. Specifically, the referenced patent attempts to solve a class of feedback processes through the deployment of a neural network that is trained by a genetic analyzer. The instant invention also is distinct over the disclosure in the improved fidelity of its model to the biological principles and related phenomena in question. The cited disclosure does not solve the problem of developing emergent behavior or provide a bottom-up approach to problem solving as in the instant invention.
The present invention is distinguished from U.S. Pat. No. 5,808,918 primarily on the basis that the patent's disclosure deals with abstractions of collected data structures at presumed and various levels of scale. The process described therein deals more with a systems engineering approach to simulation and is submitted to be top down and reductionist in nature, in contrast to the present invention's method of utilizing biological primitives in conjunction with the ontogeny engine in the example embodiment and an advanced evolutionary search method to achieve biologically accurate and adaptive emergent functionalities in the form of derived virtual tissue. Additionally, the disclosure of the patent does not provide for emergent functionality, behavior or properties, features, which comprise a fundamental principle of biological development and are an object of the present invention.
The present invention differs from U.S. Pat. No. 6,556,961 in several ways. The most significant is the field of application, namely the systems engineering approach to solving differential equations as a modeling concept and as incorporated within a cellular automata framework. Aside from the lack of any feature within the referenced patent which would support a truly emergent property or function, the sheer reliance on partial differential equations as the backbone for a proposed solution space to the class of problems which the present invention addresses is in a different context altogether. Since the thrust of the referenced patent is a somewhat automated solution for partial differential equations couched in a cellular automata environment, the prospect of achieving truly emergent functionalities, properties or behavior by the referenced patent and as required and demonstrated by the present invention is remote.
TABLE BContrast of Evolutionary Search Characteristics To Present InventionConventional GeneticImproved Model For EvolutionaryCharacteristicAlgorithmSearch In the Present InventionPopulation SizeLarge (typically 250 to 10,000+)Small (5 to 250)Mutation RateVery Low (typically 0.001% toVery Large (1.0% to 50%+)1.000%)RecombinationSexual “Crossover” At EveryRare or no sexual “crossover”GenerationEnvironmentFixed or undefinedMutable and searchableAdaptive EmergentNoneDemonstrated With ContinualFunctionalityDevelopment and RepairThe present invention exploits this advanced evolutionary search model to bring an improved result in a realistic and effective approach for virtual tissue modeling.